Recent advancements in "deepfake" techniques have paved the way for generating various media forgeries. In response to the potential hazards of these media forgeries, many researchers engage in exploring detection methods, increasing the demand for high-quality media forgery datasets. Despite this, existing datasets have certain limitations. Firstly, most datasets focus on manipulating visual modality and usually lack diversity, as only a few forgery approaches are considered. Secondly, the quality of media is often inadequate in clarity and naturalness. Meanwhile, the size of the dataset is also limited. Thirdly, it is commonly observed that real-world forgeries are motivated by identity, yet the identity information of the individuals portrayed in these forgeries within existing datasets remains under-explored. For detection, identity information could be an essential clue to boost performance. Moreover, official media concerning relevant identities on the Internet can serve as prior knowledge, aiding both the audience and forgery detectors in determining the true identity. Therefore, we propose an identity-driven multimedia forgery dataset, IDForge, which contains 249,138 video shots sourced from 324 wild videos of 54 celebrities collected from the Internet. The fake video shots involve 9 types of manipulation across visual, audio, and textual modalities. Additionally, IDForge provides extra 214,438 real video shots as a reference set for the 54 celebrities. Correspondingly, we propose the Reference-assisted Multimodal Forgery Detection Network (R-MFDN), aiming at the detection of deepfake videos. Through extensive experiments on the proposed dataset, we demonstrate the effectiveness of R-MFDN on the multimedia detection task.

翻译：近年来，"深度伪造"技术的进步为生成各类媒体伪造内容铺平了道路。为应对这些媒体伪造内容可能带来的危害，众多研究者致力于探索检测方法，从而对高质量媒体伪造数据集的需求日益增长。尽管如此，现有数据集仍存在一定局限性。首先，大多数数据集侧重于视觉模态的篡改，且通常缺乏多样性，仅考虑了少数几种伪造方法。其次，媒体在清晰度和自然度方面的质量往往不足。同时，数据集的规模也较为有限。第三，现实世界中的伪造行为通常具有身份动机，但现有数据集中所描绘人物的身份信息仍未得到充分探索。对于检测任务而言，身份信息可能是提升性能的关键线索。此外，互联网上关于相关身份的官方媒体可作为先验知识，帮助观众和伪造检测器确定真实身份。为此，我们提出了一个身份驱动的多媒体伪造数据集IDForge，该数据集包含249,138个视频片段，源自从互联网收集的54位名人的324个野生视频。伪造视频片段涉及视觉、音频和文本三种模态的9类篡改操作。此外，IDForge还额外提供了214,438个真实视频片段作为这54位名人的参考集。相应地，我们提出了参考辅助的多模态伪造检测网络（R-MFDN），旨在检测深度伪造视频。通过在所提数据集上进行大量实验，我们验证了R-MFDN在多媒体检测任务上的有效性。